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Strategies for Mapping and Cloning Quantitative Trait Genes in Rodents

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Strategies for Mapping and Cloning Quantitative Trait Genes in Rodents REVIEWS STRATEGIES FOR MAPPING AND CLONING QUANTITATIVE TRAIT GENES IN RODENTS Jonathan Flint, William Valdar, Sagiv Shifman and Richard Mott Abstract | Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin–Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents. In the 1990s, when it first became possible to map the Collaborative Cross), techniques (such as in silico chromosomal locations of quantitative trait loci (QTLs) mapping and whole-genome expression studies) and in rodents, it looked as if QTL discovery would soon analytical tools (such as quantitative complementa- lead to the identification of genes involved in any med- tion tests and Yin–Yang crosses, see below for more ically important phenotype that could be modelled in information). mice or rats1–4. In this review, we discuss the realized or potential More than 10 years later, the field of QTL analysis is success of new approaches to identify genes that under- heading towards a crisis. The National Center for lie QTLs.We focus on methods to identify genetic vari- Biotechnology Information (NCBI) lists 700 QTLs that ants that have only a small effect on the phenotype (by are found in rats (see the Entrez Gene web site in the which we mean that the variants account for 10% or Online links box), and the mouse genome database cur- less of the phenotypic variation of a quantitative rently contains 2,050 mouse QTLs (see the Mouse trait). In the past, robust and reliable detection of Genome Informatics web site in the Online links box). small-effect QTLs has been a problem5;but, as should Compare these figures with the number of candidate be evident from the number of QTLs that have been genes that are proposed to underlie QTLs in rodents reported, difficulties in QTL detection are not imped- (TABLES 1,2).Ifwe uncritically accept all the claims as cor- ing research. The methods might not be the most effi- rect, only about 20 genes have been identified. Even if no cient, but there is no doubt that QTLs can be detected more QTLs are mapped, at the present rate of progress at high levels of significance and that the findings can (20 genes identified in 15 years) it will take 1,500 years to be replicated, even for behavioural phenotypes6–9. Wellcome Trust Centre for find all the genes that underlie known QTLs. Although it is likely that only a small fraction of the Human Genetics, Oxford Several recent developments have been published total number of QTLs that segregate in inbred strain University, Roosevelt Drive, that might make what is currently almost impossible crosses are currently known to us (not to mention Oxford OX3 7BN, United more tractable. These include new genomic resources those in outbred stocks), without the ability to deter- Kingdom. Correspondence to J.F. (for example, the availability of sequences and mine the genes they represent, their detection is of e-mail: [email protected] sequence variants), animal resources (for example, limited interest. We urgently need advances in gene, doi:10.1038/nrg1576 chromosome substitution strains and the proposed not QTL, identification. NATURE REVIEWS | GENETICS VOLUME 6 | APRIL 2005 | 271 © 2005 Nature Publishing Group REVIEWS Table 1 | Cloned quantitative trait loci Gene symbol Gene name Phenotype/function Variation (%) Gene identification method Reference* Alox15 Arachidonate Abnormal bone mass 4% Expression differences, analysis of 107 12/15 lipoxygenase knockout alleles, pharmacological inhibition Rgs2 Regulator of Fear-related behaviour 5% Outbred mice, QTL–knockout interaction 31 G-protein signalling 2 Cyp11b1 Cytochrome P450, Abnormal blood pressure 5% Congenics, sequence variants 154 subfamily 11B, polypeptide 1 Lipc Hepatic lipase Obesity 7% Multiple crosses, knockout interaction 155 C5 Complement Experimental allergic 8% Expression differences, loss-of-function 106 component 5 asthma mutation Ace2 Angiotensin 1 Abnormal blood pressure 12% Knockout phenotype 156 converting enzyme 2 (peptidyl-dipeptidase A) Apoa2 Apolipoprotein A2 HDL cholesterol 23% Multiple-strain haplotypes, coding 88 sequence variants Mpdz Multiple PDZ domain Pentobarbital drug 25% Genotype-dependent differences in 16 protein withdrawal with seizures coding sequence and expression Ncf1 Neutrophil cytosolic Severe arthritis 25% Functional sequence change, 157 factor 1 pharmacological treatment Kcnj10 Potassium inwardly- Susceptibility to seizure 25% Coding sequence variants, multiple-strain 15 rectifying channel, haplotypes subfamily J, member 10 Ptprj (Scc1)Protein Susceptibility to colon 29% Recombinant haplotypes, sequence 158 tyrosine phosphatase cancer differences, loss of heterozygosity receptor type, J polypeptide in human cancer Tas1r3 Taste receptor, type 1, Saccharin response 30% Multiple-strain haplotypes, coding 159 member 4 sequence variants Nppa Natriuretic peptide Cardiac hypertrophy 44% Promoter variants increase gene 160 precursor A expression Cd36 Fatty acid translocase Abnormal fatty acid 50% Expression differences, loss-of-function 161 metabolism mutation, transgene complementation Pla2g2a Phospholipase A2, group IIA Modifier of tumours in 50% Frameshift loss of function and transgene 162 (platelets, synovial fluid) intestinal cancer complementation (cosmid) Mtap1a Microtubule-associated Modifier of hearing loss 57% Coding sequence variants, transgene 163 protein 1a complementation (cosmid) *The first publication of this material. HDL, high density lipoprotein. Before proceeding, we should also be clear about probable effectiveness of the new approaches? To answer what we exclude from our discussion. In this paper, this question, we need to define success, which is not so we do not consider artificially induced mutations that straightforward. Two recent reviews that attempt this task present as QTLs, such as those that are due to mutage- identify 21 genes in total, but agree about the identifica- nesis, although it has been suggested that mutagenesis tion of only four genes in mice and five in rats11,12.As in the mouse can be used to determine the genetic many have pointed out, there is no single proof that con- basis of quantitative traits instead of QTL mapping10. firms a gene at a QTL and, to a large extent, each case has Screening mice for the quantitative effects of induced to be evaluated on its own strengths13,14.Replacing one mutations is doubtless an efficient method for the allele with another at a locus through targeted mutagene- identification of genes that are involved in the expres- sis with subsequent functional analysis provides a strin- sion of a phenotype, quantitative or otherwise. gent test, as does complementation, but these procedures However, our concern here is with the subset of poly- might not be possible (or even necessary) in every case: morphic loci that give rise to phenotypic variation in alternative evidence can provide sufficient proof that the natural populations (although we admit that labora- gene has been found. tory mouse populations are in many respects unnat- There are also issues about what to include as a ural). Mutagenesis does not distinguish between QTL. Strictly speaking, a QTL is any locus that con- these loci and the large number of loci that are func- tributes to a phenotype that is measured quantitatively. tionally invariant in nature, but in which mutations However, some investigators use a quantitative mea- can be induced. sure for a phenotype that others assess qualitatively (for example, susceptibility to cancer can be assessed as Lessons from previous success stories affected or not affected, or by a quantitative measure, Are there any lessons from success stories in the field of such as the number of tumours). To be comprehen- QTL mapping that might allow us to determine the sive, we have summarized data on candidate genes in 272 | APRIL 2005 | VOLUME 6 www.nature.com/reviews/genetics © 2005 Nature Publishing Group REVIEWS Table 2 | Cloned susceptibility loci Gene Gene Phenotype Penetrance Method of detection Reference* symbol Cblb Casitas B-lineage Type 1 diabetes High Nonsense mutation, transgene 164 lymphoma b complementation (cDNA) Stk6 Serine–threonine Skin tumour Low Multiple-strain haplotypes, 74 kinase 6 susceptibility outbred mice, expression difference Ctla4 Cytotoxic T-lymphocyte- Autoimmune Low Sequence variants, association 165 associated protein 4 disease studies in humans Il2 Interleukin 2 Type 1 diabetes Low Coding sequence variants, 166 differences in electrophoretic patterns Pctr1 Plasmacytoma resistance 1 Susceptibility to Low Coding sequence variants, 167 plasmacytoma protein variant less active B2m β-2-Microglobulin Type 1 diabetes Low Coding sequence variant, 168 transgene complementation (plasmid) *The first publication of this material. TABLES 1,2. TABLE 1 lists candidate genes at loci where candidacy of Mpdz,but had a much smaller interval, the effects are expressed as the percentage of the total with only three known and three predicted genes to val- phenotypic variance. TABLE 2 lists candidate genes at idate, and so
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